arXiv — cs.AI preprintsInternational2 October 2026
Looping Beyond Twice: A Scalable Recipe for Looped Mixture-of-Experts
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arXiv:2610.01153v1 Announce Type: cross Abstract: Looped Transformers introduce recurrent depth as a new scaling axis for LLMs: by repeatedly applying shared Transformer blocks, they increase effective depth without increasing parameter count. However, the benefits of looping remain unclear for large MoE LLMs under FLOPs-matched comparisons. The main reason is that the gains from additional iterations diminish quickly and can even turn into degradation, so the extra FLOPs spent on looping yield little substantial improvement. Consequently, prior work typically settles on two loops. We identify
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